Bibliographic record
Abstract
Scholars have acknowledged the contribution of video gaming to complex forms of learning, identifying links between gaming and engagement, experiential learning spaces, problem-solving, strategies, transliteracy reflectivity, critical literacy, and metacognitive thinking. Despite this movement toward the inclusion of video gaming in literacy teaching, concerns about certain risks raised by scholars have slowed the adoption of video games to foster learning. Existing research in video gaming practices, especially for boys, tend to focus on risks associated with boys’ gaming choices. For example, boys who interact with video games and apply that knowledge to in-school practice are considered problematic due to scholars’ significant reservations about stereotypical themes (such as themes of power, violence and misogyny) embedded in video game plots and characters. Despite this, research continues to emerge offering insights to educators that gaming and literacy are not on opposite ends of the literacy learning spectrum but rather represent a highly unified multimodal foundation (Beavis, 2012; Gee, 2014; Squire, 2013; Steinkuehler, Squire, & Barab, 2012). Therefore, in this chapter I present some of my findings from my multi-case ethnographic study which examined the unique experiences of four boys engaged with video gaming in two different contexts: a community center and an after-school video club. This chapter also addresses how researchers can collaborate with school educators to support practical classroom strategies using multiliteracies resources, such as the Learning by Design framework (Cope & Kalantzis, 2016).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.035 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".